Solar Radiation Prediction Model for the Yellow River Basin with Deep Learning

Solar radiation is the main source of energy on Earth’s surface. It very important for environment and ecology, water cycle crop growth. Therefore, it to obtain accurate solar data. In this study, we use highest temperature Tmax, lowest Tmin, average Tavg, wind speed U, relative humidity RH, sunshine duration H maximum Hmax as input variables construct a deep learning prediction model in Yellow River Basin. compared with recommended corrected values widely used Å-P method. The results show that: (1) correction equation are better upstream downstream Basin but worse midstream. (2) result far than that using FAO-56 value. best Basin: R2 increases from 0.894 0.934; MSE, RMSE MAE decrease by 43.12%, 27.73% 25.80%, respectively. comes second: 0.888 0.921; 33.27%, 20.02% 19.04%, midstream worst: 0.869 0.874; ?0.50%, 0.07% 3.82%, (3) those correction. 0.889 0.921. 22.11%, 11.84% 8.94%, 0.900 0.934, 13.21%, 11.40% 5.55%, Basin, correction: 0.870 0.874, ?24.93%, ?10.83% ?11.56%,